Artificial Intelligence Architect

Experience Required: 12–18 Years in Software Engineering | 7+ Years in AI/ML & Generative AI Utility Domain/ Water- treatment client's experience is must Role Overview We are seeking a highly accomplished AI Architect with deep expertise in Google AI technologies and Generative AI to lead the design and implementation of enterprise-scale AI solutions. This role requires strong architectural vision, hands‑on technical depth, and leadership in building production‑grade AI systems leveraging LLMs, SLMs, and multi‑agent frameworks . The ideal candidate will drive AI strategy, define scalable architectures, and lead cross‑functional teams in delivering cutting‑edge AI‑powered applications using the Google Cloud ecosystem , modern AI frameworks, and robust MLOps practices. Key Responsibilities 1. AI Architecture & Strategy Define end‑to‑end AI/GenAI architecture for enterprise-grade applications. Establish best practices for LLM/SLM adoption, multi‑agent systems, and RAG architectures . Drive AI platform strategy leveraging Google Cloud (Vertex AI, GKE, Cloud Run) . Lead architecture reviews, technical governance, and design standards. 2. LLM / SLM & Generative AI Solutions Architect solutions using commercial LLMs such as Gemini, GPT, and Claude . Design scalable systems using open‑source models (Mixtral, Mistral, Gemma, Phi‑3) . Define strategies for fine‑tuning (LoRA, QLoRA, PEFT) and model optimization. Oversee model evaluation frameworks and benchmarking (HELM, lm‑eval, RAGAS). Lead adoption of: Vertex AI for model lifecycle management Google Workspace integrations (Docs, Sheets, Gmail, Drive, Meet) Architect solutions using BigQuery, Lakehouse, and Vector Databases . Design scalable MLOps pipelines for training, deployment, and monitoring. Define CI/CD strategies for AI systems using GitHub Actions / GitLab CI . Establish observability frameworks using LangSmith, MLflow, Weights & Biases . Optimize infrastructure cost and performance across cloud and hybrid environments. 5. Multi-Agent Systems & AI Frameworks Architect complex workflows using: Semantic Kernel for multi‑agent orchestration Design intelligent automation pipelines and agent collaboration patterns. Define data ingestion, transformation, and governance strategies. Architect semantic search and knowledge retrieval systems. Define backend architecture using FastAPI / Node.js APIs . Architect API management and security using Apigee / MuleSoft . Guide frontend architecture using React / Angular for AI‑driven applications. 8. Engineering Leadership Provide technical leadership and mentorship to AI/ML engineers. Collaborate with product, data, and engineering teams for solution delivery. Lead design documentation, architecture diagrams, and technical roadmaps. Ensure adherence to coding standards, testing, and quality frameworks. 9. Deployment & Infrastructure GCP (Vertex AI, GKE, Cloud Run) Hybrid and on‑prem environments Edge AI use cases Ensure scalability, reliability, and security of AI systems. 10. AI Governance & Responsible AI Define frameworks for AI ethics, bias mitigation, and explainability . Establish governance for model lifecycle, monitoring, and compliance . Implement safeguards for hallucination detection and output validation. Required Qualifications 12–18 years of software engineering experience. 7+ years in AI/ML with strong focus on Generative AI and LLMs . Deep expertise in Google AI ecosystem (Vertex AI, Gemini, ADK, AI Studio) . Strong experience in LLMs, SLMs, RAG, and multi‑agent architectures . Proficiency in Python and familiarity with Node.js . Hands‑on experience with MLOps, CI/CD, and cloud‑native architecture (GCP) . Preferred Qualifications Google Cloud Certifications ( Professional ML Engineer / Cloud Architect ). Experience contributing to open‑source AI/ML projects . Expertise in edge AI and hybrid cloud deployments . Experience building enterprise AI platforms or COEs . Strong leadership experience mentoring and scaling AI teams. Key Skills Summary Generative AI (LLMs, SLMs, RAG, Agents) Google Cloud AI Stack (Vertex AI, Gemini, ADK) AI Frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel) MLOps & Observability (MLflow, W&B, LangSmith) Data & Vector DBs (BigQuery, ChromaDB, Vector Search) #J-18808-Ljbffr